Quick guide
| from | to | best.effort |
|---|---|---|
| 80_10a | file 001 | 80_10a |
| 80_10b | file 002 | 80_10b |
| 80_10c | file 003 | 80_10c |
| 80_10d | file 004 | 80_10d |
| 80_12a | file 005 | 80_12a |
| 80_12c | file 006 | 80_12c |
| 80_12d | file 007 | 80_12d |
| 80_14a | file 008 | 80_14a |
| 80_18a | file 009 | 80_18a |
| 80_18b | file 010 | 80_18b |
| 80_18d | file 011 | 80_18d |
| 80_20a | file 012 | 80_20a |
| 80_20b | file 013 | 80_20b |
| 80_20c | file 014 | 80_20c |
| 80_20d | file 015 | 80_20d |
| 80_7a | file 016 | 80_7a |
| 80_7b | file 017 | 80_7b |
| 80_7c | file 018 | 80_7c |
| 80_7d | file 019 | 80_7d |
| C_18b | file 020 | C_18b |
| C_18c | file 021 | C_18c |
| C_7b | file 022 | C_7b |
| 2_10b | file 023 | 2_10b |
| 2_10d | file 024 | 2_10d |
| 2_12a | file 025 | 2_12a |
| 2_12b | file 026 | 2_12b |
| 2_12c | file 027 | 2_12c |
| 2_12d | file 028 | 2_12d |
| 2_18a | file 029 | 2_18a |
| 2_18b | file 030 | 2_18b |
| 2_18d | file 031 | 2_18d |
| 2_20a | file 032 | 2_20a |
| 2_20b | file 033 | 2_20b |
| 2_20c | file 034 | 2_20c |
| 2_20d | file 035 | 2_20d |
| 2_7a | file 036 | 2_7a |
| 2_7b | file 037 | 2_7b |
| 2_7c | file 038 | 2_7c |
| tkQE171103_JL601_Control_1_10_02 | file 039 | tkQE171103_JL601_Control_1_10_02 |
| tkQE171103_JL601_Control_1_12 | file 040 | tkQE171103_JL601_Control_1_12 |
| tkQE171103_JL601_Control_1_18 | file 041 | tkQE171103_JL601_Control_1_18 |
| tkQE171103_JL601_Control_2_12 | file 042 | tkQE171103_JL601_Control_2_12 |
| tkQE171103_JL601_Control_2_18 | file 043 | tkQE171103_JL601_Control_2_18 |
| tkQE171103_JL601_Control_3_18_02 | file 044 | tkQE171103_JL601_Control_3_18_02 |
| tkQE171103_JL601_NCBP3_1_10_02 | file 045 | tkQE171103_JL601_NCBP3_1_10_02 |
| tkQE171103_JL601_NCBP3_1_12 | file 046 | tkQE171103_JL601_NCBP3_1_12 |
| tkQE171103_JL601_NCBP3_1_18 | file 047 | tkQE171103_JL601_NCBP3_1_18 |
| tkQE171103_JL601_NCBP3_2_18 | file 048 | tkQE171103_JL601_NCBP3_2_18 |
| tkQE171103_JL601_NCBP3_2_7_02 | file 049 | tkQE171103_JL601_NCBP3_2_7_02 |
| tkQE171103_JL601_NCBP3_3_10 | file 050 | tkQE171103_JL601_NCBP3_3_10 |
| tkQE171103_JL601_NCBP3_3_12 | file 051 | tkQE171103_JL601_NCBP3_3_12 |
| tkQE171103_JL601_NCBP3_3_18_02 | file 052 | tkQE171103_JL601_NCBP3_3_18_02 |
| tkQE171103_JL601_NCBP3_3_7_02 | file 053 | tkQE171103_JL601_NCBP3_3_7_02 |
| 2C_12a | file 054 | 2C_12a |
| tkQE171103_JL601_Control_1_7 | file 055 | tkQE171103_JL601_Control_1_7 |
| tkQE171103_JL601_Control_2_10 | file 056 | tkQE171103_JL601_Control_2_10 |
| tkQE171103_JL601_NCBP3_1_7 | file 057 | tkQE171103_JL601_NCBP3_1_7 |
| tkQE171103_JL601_NCBP3_2_10 | file 058 | tkQE171103_JL601_NCBP3_2_10 |
| 80_14d | file 059 | 80_14d |
| C_14d | file 060 | C_14d |
| C_18a | file 061 | C_18a |
| 2C_18a | file 062 | 2C_18a |
| tkQE171103_JL601_Control_3_10 | file 063 | tkQE171103_JL601_Control_3_10 |
| tkQE171103_JL601_Control_3_12 | file 064 | tkQE171103_JL601_Control_3_12 |
| 80_14b | file 065 | 80_14b |
| C_20a | file 066 | C_20a |
| tkQE171103_JL601_NCBP3_2_12 | file 067 | tkQE171103_JL601_NCBP3_2_12 |
| 2C_12b | file 068 | 2C_12b |
| 2C_12d | file 069 | 2C_12d |
| 2C_14a | file 070 | 2C_14a |
| 2C_14b | file 071 | 2C_14b |
| 2C_18c | file 072 | 2C_18c |
| 2C_20a | file 073 | 2C_20a |
| 2C_20b | file 074 | 2C_20b |
| 2C_20c | file 075 | 2C_20c |
| 2C_20d | file 076 | 2C_20d |
| 2C_7c | file 077 | 2C_7c |
| 2C_7d | file 078 | 2C_7d |
| C_10c | file 079 | C_10c |
| C_10d | file 080 | C_10d |
| C_20d | file 081 | C_20d |
| C_7d | file 082 | C_7d |
| 80_14c | file 083 | 80_14c |
| C_14a | file 084 | C_14a |
| C_14c | file 085 | C_14c |
| C_20c | file 086 | C_20c |
| C_10b | file 087 | C_10b |
| C_12a | file 088 | C_12a |
| C_14b | file 089 | C_14b |
| C_7a | file 090 | C_7a |
| C_20b | file 091 | C_20b |
| 80_18c | file 092 | 80_18c |
| C_12c | file 093 | C_12c |
| C_18d | file 094 | C_18d |
| C_7c | file 095 | C_7c |
| 2C_12c | file 096 | 2C_12c |
| 2C_14c | file 097 | 2C_14c |
| 2C_18d | file 098 | 2C_18d |
| 2C_7a | file 099 | 2C_7a |
| 2_14a | file 100 | 2_14a |
| 2_14b | file 101 | 2_14b |
| 2_14c | file 102 | 2_14c |
| 2_14d | file 103 | 2_14d |
| 2C_10b | file 104 | 2C_10b |
| tkQE171103_JL601_Control_2_7_02 | file 105 | tkQE171103_JL601_Control_2_7_02 |
| tkQE171103_JL601_Control_3_7_02 | file 106 | tkQE171103_JL601_Control_3_7_02 |
| 2C_14d | file 107 | 2C_14d |
| 2_10a | file 108 | 2_10a |
| C_12d | file 109 | C_12d |
| 2C_18b | file 110 | 2C_18b |
| C_10a | file 111 | C_10a |
| 2_10c | file 112 | 2_10c |
| 2C_10a | file 113 | 2C_10a |
| 2C_10c | file 114 | 2C_10c |
| 2C_10d | file 115 | 2C_10d |
| 2C_7b | file 116 | 2C_7b |
(excludes contaminants)
Principal components plots of experimental groups (as defined during MaxQuant configuration).
This plot is shown only if more than one experimental group was defined. If LFQ was activated in MaxQuant, an additional PCA plot for LFQ intensities is shown. Similarly, if iTRAQ/TMT reporter intensities are detected.
Since experimental groups and Raw files do not necessarily correspond 1:1, this plot cannot use the abbreviated Raw file names, but instead must rely on automatic shortening of group names.
Heatmap score: none (since data source proteinGroups.txt is not related 1:1 to Raw files)
PTXQC will explicitly show the five most abundant external protein contaminants (as detected via MaxQuant’s contaminants FASTA file) by Raw file, and summarize the remaining contaminants as ‘other’. This allows to track down which proteins exactly contaminate your sample. Low contamination is obviously better. The ‘Abundance class’ models the average peptide intensity in each Raw file and is visualized using varying degrees of transparency. It is not unusual to see samples with low sample content to have higher contamination. If you see only one abundance class (‘mid’), this means all your Raw files have roughly the same peptide intensity distribution.
Heatmap score [EVD: Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
User defined contaminant plot based on peptide intensities and counts. Usually used for Mycoplasma detection, but can be used for an arbitrary (set of) proteins.
All proteins (and their peptides) which contain the search string from the YAML file are considered contaminants. The contaminant’s search string is searched in the full FASTA header in proteinGroups.txt. If proteinGroups.txt is not available/found, only protein identifiers can be considered. The search realm used is given in the plot subtitle. You should choose the contaminant name to be distinctive. Only peptides belonging to a single protein group are considered when computing the fractions (contaminant vs. all), since peptides shared across multiple groups are potentially false positives.
Two abundance measures are computed per Raw file:
If the intensity fraction exceeds the threshold (indicated by the dashed horizontal line) a contamination is assumed.
For each Raw file exceeding the threshold an additional plot giving cumulative Andromeda peptide score distributions is shown. This allows to decide if the contamination is true. Contaminant scores should be equally high (or higher), i.e. to the right, compared to the sample scores. Each graph’s subtitle is augmented with a p-value of the Kologorov-Smirnoff test of this data (Andromeda scores of contaminant peptides vs. sample peptides). If the p-value is high, there is no score difference between the two peptide populations. In particular, the contaminant peptides are not bad-scoring, random hits. These p-values are also shown in the first figure for each Raw file. Note that the p-value is purely based on Andromeda scores and is independent of intensity or spectral counts.
Heatmap score [EVD: Contaminant
RSD 4.5% (expected < 5%)
Peptide precursor intensity per Raw file from evidence.txt. Low peptide intensity usually goes hand in hand with low MS/MS identifcation rates and unfavourable signal/noise ratios, which makes signal detection harder. Also instrument acquisition time increases for trapping instruments.
Failing to reach the intensity threshold is usually due to unfavorable column conditions, inadequate column loading or ionization issues. If the study is not a dilution series or pulsed SILAC experiment, we would expect every condition to have about the same median log-intensity (of 223.0). The relative standard deviation (RSD) gives an indication about reproducibility across files and should be below 5%.
Depending on your setup, your target thresholds might vary from PTXQC’s defaults. Change the threshold using the YAML configuration file.
Heatmap score [EVD: Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 221 of 223 gives score 0.25.
RSD 5.3% (w/o zero int.; expected < 5%)473.8% [high RSD –> few peptides])
Intensity boxplots by experimental groups. Groups are user-defined during MaxQuant configuration. This plot displays a (customizable) threshold line for the desired mean intensity of proteins. Groups which underperform here, are likely to also suffer from a worse MS/MS id rate and higher contamination due to the lack of total protein loaded/detected. If possible, all groups should show a high and consistent amount of total protein. The height of the bar correlates to the number of proteins with non-zero abundance.
Contaminants are shown as overlayed yellow boxes, whose height corresponds to the number of contaminant proteins. The position of the box gives the intensity distribution of the contaminants.
Heatmap score: none (since data source proteinGroups.txt is not related 1:1 to Raw files)
RSD 12% (w/o zero int.; expected < 5%)1077% [high RSD –> few peptides])
Label-free quantification (LFQ) intensity boxplots by experimental groups. Groups are user-defined during MaxQuant configuration. This plot displays a (customizable) threshold line for the desired mean of LFQ intensity of proteins. Raw files which underperform in Raw intensity, are likely to show an increased mean here, since only high-abundance proteins are recovered and quantifyable by MaxQuant in this Raw file. The remaining proteins are likely to receive an LFQ value of 0 (i.e. do not contribute to the distribution). The height of the bar correlates to the number of proteins with non-zero abundance.
Contaminants are shown as overlayed yellow boxes, whose height corresponds to the number of contaminant proteins. The position of the box gives the intensity distribution of the contaminants.
Heatmap score: none (since data source proteinGroups.txt is not related 1:1 to Raw files)
Charge distribution per Raw file. For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544 ). The charge distribution should be similar across Raw files. Consistent charge distribution is paramount for comparable 3D-peak intensities across samples.
Heatmap score [EVD: Charge]: Deviation of the charge 2 proportion from a representative Raw file (‘qualMedianDist’ function).
External protein contamination should be controlled for, therefore MaxQuant ships with a comprehensive, yet customizable protein contamination database, which is searched by MaxQuant by default. PTXQC generates a contamination plot derived from the proteinGroups (PG) table showing the fraction of total protein intensity attributable to contaminants. The plot employs transparency to discern differences in the group-wise summed protein abundance. This allows to delineate a high contamination in high complexity samples from a high contamination in low complexity samples (e.g. from in-gel digestion). If you see only one abundance class (‘mid’), this means all your groups have roughly the same summed protein intensity. Note that this plot is based on experimental groups, and therefore may not correspond 1:1 to Raw files.
Heatmap score: none (since data source proteinGroups.txt is not related 1:1 to Raw files)
Reaching TopN on a regular basis indicates that all sections of the LC gradient deliver a sufficient number of peptides to keep the instrument busy. This metric somewhat summarizes ‘TopN over RT’.
Heatmap score [MS2 Scans: TopN high]: rewards if TopN was reached on a regular basis (function qualHighest)
TopN over retention time. Similar to ID over RT, this metric reflects the complexity of the sample at any point in time. Ideally complexity should be made roughly equal (constant) by choosing a proper (non-linear) LC gradient. See Moruz 2014, DOI: 10.1002/pmic.201400036 for details.
Heatmap score [MS2 Scans: TopN over RT]: Rewards uniform (function Uniform) TopN events over time.
Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization. See Moruz 2014, DOI: 10.1002/pmic.201400036 for details.
Heatmap score [EVD: ID rate over RT]: Scored using ‘Uniform’ scoring function, i.e. constant receives good score, extreme shapes are bad.
One parameter of optimal and reproducible chromatographic separation is the distribution of widths of peptide elution peaks, derived from the evidence table. Ideally, all Raw files show a similar distribution, e.g. to allow for equal conditions during dynamic precursor exclusion, RT alignment or peptide quantification.
Heatmap score [EVD: RT Peak Width]: Scored using BestKS function, i.e. the D statistic of a Kolmogoriv-Smirnoff test.
Ion injection time score - should be as low as possible to allow fast cycles. Correlated with peptide intensity. Note that this threshold needs customization depending on the instrument used (e.g., ITMS vs. FTMS).
Heatmap score [MS2 Scans: Ion Inj Time]: Linear score as fraction of MS/MS below the threshold.
MS/MS identifications can be ‘bad’ for a couple of reasons. It could be computational, i.e. ID rates are low because you specified the wrong protein database or modifications (not our concern here). Another reason is low/missing signals for fragment ions, e.g. due to bad (quadrupole/optics) ion transmission (charging effects), too small isolation windows, etc.
Hence, we plot the TIC and base peak intensity of all MS/MS scans (incl. unidentified ones) per Raw file. Depending on the setup, these intensities can vary, but telling apart good from bad samples should never be a problem. If you only have bad samples, you need to know the intensity a good sample would reach.
To automatically score this, we found that the TIC should be 10-100x larger than the base peak, i.e. there should be many other ions which are roughly as high (a good fragmentation ladder). If there are only a few spurious peaks (bad MS/MS), the TIC is much lower. Thus, we score the ratio BP * 10 > TIC (this would be 100% score). If it’s only BP * 3 < TIC, we say this MS/MS failed (0%). Anything between 3x and 10x gets a score in between. The score for the Raw file is computed as the median score across all its MS/MS scans.
Heatmap score [MS2 Scans: Intensity]: Linear score (0-100%) between 3 < (TIC / BP) < 10.
An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.
Heatmap score [EVD: MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.
Mass accurary before calibration. Outliers are marked as such (‘out-of-search-tol’) using ID rate and standard deviation as additional information (if available). If any Raw file is flagged ‘failed’, increasing MaxQuant’s first-search tolerance (20ppm by default, here: 20.0 ppm) might help to enable successful recalibration. A bug in MaxQuant sometimes leads to excessively high ppm mass errors (>104) reported in the output data. However, this can sometimes be corrected for by re-computing the delta mass error from other data. If this is the case, a warning (‘bugfix applied’) will be shown.
Heatmap score [EVD: MS Cal Pre (20.0)]: the centeredness (function CenteredRef) of uncalibrated masses in relation to the search window size.
Precursor mass accuracy after calibration. Failed samples from precalibration data are still marked here. Ppm errors should be centered on zero and their spread is expected to be significantly smaller than before calibration.
Heatmap score [EVD: MS Cal-Post]: The variance and centeredness around zero of the calibrated distribution (function GaussDev).
Looking at the identification rates per scan event (i.e. the MS/MS scans after a survey scan) can give hints on how well scheduled precursor peaks could be fragmented and identified. If performance drops for the later MS/MS scans, then the LC peaks are probably not wide enough to deliver enough eluent or the intensity threshold to trigger the MS/MS event should be lowered (if LC peak is already over), or increased (if LC peak is still to weak to collect enough ions).
Heatmap score [MS2 Scans: TopN ID over N]: Rewards uniform identification performance across all scan events.
compared to all peptides seen in experiment
Missing peptide intensities per Raw file from evidence.txt. This metric shows the fraction of missing peptides compared to all peptides seen in the whole experiment. The more Raw files you have, the higher this fraction is going to be (because there is always going to be some exotic [low intensity?] peptide which gets [falsely] identified in only a single Raw file). A second plot shows how many peptides (Y-axis) are covered by at least X Raw files. A third plot shows the density of the observed (line) and the missing (filled area) data. To reconstruct the distribution of missing values, an imputation strategy is required, so the argument is somewhat circular here. If all Raw files are (technical) replicates, i.e. we can expect that missing peptides are indeed present and have an intensity similar to the peptides we do see, then the median is a good estimator. This method performs a global normalization across Raw files (so their observed intensitiy distributions have the same mean), before computing the imputed values. Afterwards, the distributions are de-normalized again (shifting them back to their) original locations – but this time with imputed peptides.
Peptides obtained via Match-between-run (MBR) are accounted for (i.e. are considered as present = non-missing). Thus, make sure that MBR is working as intended (see MBR metrics).
Warning: this metric is meaningless for fractionated data! TODO: compensate for lower scores in large studies (with many Raw files), since peptide FDR is accumulating!?
Heatmap score [EVD: Pep Missing]: Linear scale of the fraction of missing peptides.
Number of unique (i.e. not counted twice) peptide sequences including modifications (after FDR) per Raw file. A configurable target threshold is indicated as dashed line.
If MBR was enabled, three categories (‘genuine (exclusive)’, ‘genuine + transferred’, ‘transferred (exclusive)’ are shown, so the user can judge the gain that MBR provides.
Peptides in the ‘genuine + transferred’ category were identified within the Raw file by MS/MS, but at the same time also transferred to this Raw file using MBR. This ID transfer can be correct (e.g. in case of different charge states), or incorrect – see MBR-related metrics to tell the difference. Ideally, the ‘genuine + transferred’ category should be rather small, the other two should be large.
If MBR would be switched off, you can expect to see the number of peptides corresponding to ‘genuine (exclusive)’ + ‘genuine + transferred’. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).
Heatmap score [EVD: Pep Count (>15000)]: Linear scoring from zero. Reaching or exceeding the target threshold gives a score of 100%.
Number of Protein groups (after FDR) per Raw file. A configurable target threshold is indicated as dashed line.
If MBR was enabled, three categories (‘genuine (exclusive)’, ‘genuine + transferred’, ‘transferred (exclusive)’ are shown, so the user can judge the gain that MBR provides. Here, ‘transferred (exclusive)’ means that this protein group has peptide evidence which originates only from transferred peptide IDs. The quantification is (of course) always from the local Raw file. Proteins in the ‘genuine + transferred’ category have peptide evidence from within the Raw file by MS/MS, but at the same time also peptide IDs transferred to this Raw file using MBR were used. It is not unusual to see the ‘genuine + transferred’ category be the rather large, since a protein group usually has peptide evidence from both sources. To see of MBR worked, it is better to look at the two MBR-related metrics.
If MBR would be switched off, you can expect to see the number of protein groups corresponding to ‘genuine (exclusive)’ + ‘genuine + transferred’. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).
Heatmap score [EVD: Prot Count (>3500)]: Linear scoring from zero. Reaching or exceeding the target threshold gives a score of 100%.